Mass Transit Railway (Hong Kong)
The Mass Transit Railway (MTR) is a major public transport network serving Hong Kong. Operated by the MTR Corporation Limited (MTRCL), it consists of heavy rail, light rail, and feeder bus service centred on an 11-line rapid transit network serving the urbanised areas of Hong Kong Island, Kowloon, and the New Territories. The system included 230.9 km (143.5 mi) of rail in 2018 with 163 stations, including 95 heavy rail stations and 68 light rail stops. The MTR is one of the most profitable metro systems in the world; it had a farebox recovery ratio of 187 per cent in 2015, the world's highest. The MTR was ranked the number one metro system in the world by CNN in 2017.
The MTR partnership started in January 2013 and ended in December 2019 with near capacity operations informed by big transit data analytics as its central theme. Through this partnership, MTR used the Transit Lab’s vast expertise to develop solutions that support MTR’s growth and ability to provide innovative services to its customers. The broad goal was to gain a better understanding of how the MTR system operates at “near capacity” and investigate means of dealing with capacity constraints more effectively, mitigating the impacts. Data from various sources, supported by appropriate models, are used to provide insights into system operations taking a customer-centric view. Examples of the research areas include:
1. Estimation of denied boarding due to capacity constraints and station crowding. The model has replaced expensive, manual surveys that MTR used to conduct several times a year to collect this information.
2. Network performance tools that evaluate system performance, measured by various metrics, under different operating conditions and used for schedule design, and analysis of dispatching strategies.
3. Design, evaluation, and monitoring of transit travel demand management strategies in the form of promotions that encourage behavior shifts away from peak periods.
4. Advanced visualization capabilities that provide valuable insights to managers.
5. Predictive analytics of station and OD demands, as well as expected denied boarding, used to proactively deal with crowd management, respond to incidents, and provide more individualized customer information.
Under this research the MIT-NU team also developed a number of tools related to the above areas that are currently adopted and used in daily operations.